
%reload_ext autoreload
%autoreload 2

import sys
import os

import numpy as np
import matplotlib.pyplot as plt

plt.rcParams.update({'figure.max_open_warning': 0})


from viz.plotting.utils import resize_by_tradeoff, color_by_dist, color_by_cv, enhance_color, cfs

def load_props(path):
    r"""
    Load CV, Mu, Ik and S from path.
    """
    CV = None
    cvpathf = os.path.join(path, "cv.csv")
    if os.path.exists(cvpathf):
        CV = np.loadtxt(cvpathf)

    # load the Mu values
    Mu = None
    mupathf = os.path.join(path, "mu.csv")
    if os.path.exists(mupathf):
        Mu = np.loadtxt(mupathf)

    # load the knee indices
    Ik = None
    kpathf = os.path.join(path, "muid.csv")
    if os.path.exists(kpathf):
        Ik = np.loadtxt(kpathf, dtype=int)
        
    return CV, Mu, Ik

def decide_size_color(pf, dim, CV, Mu, Ik):
    r"""
    Decide point-size and color from `CV`, `Mu` and `Ik`
    """
    # decide point-size
    S = 1
    if Mu is not None and Ik is not None:
        S = resize_by_tradeoff(Mu, k=Ik)

    # decide colors
    # C = default_color(F.shape[0], alpha=0.5)
    M = np.mean(F, axis=0)
    C, D = color_by_dist(F, M)
    
    # if CV is available, use CV to color
    if CV is not None:
        pfname = pf.split('-')[0]
        if 'dtlz8' in pfname:
            C = color_by_cv(CV, factor=cfs[pfname][int(dim[:-1])])
        else:
            C = color_by_cv(CV)

    # enhance knee points
    if Ik is not None:
        C = enhance_color(C, Ik)
    return S, C, D


%matplotlib inline
from viz.plotting import pcp
from viz.utils import transform as tr

pfs = {'dtlz2': ['3d', '4d', '8d'], \
       'dtlz2-nbi': ['3d', '4d', '8d'], \
       'debmdk': ['3d', '4d', '8d'], \
       'debmdk-nbi': ['3d', '4d', '8d'], \
       'debmdk-all': ['3d', '4d', '8d'], \
       'debmdk-all-nbi': ['3d', '4d', '8d'], \
       'dtlz8': ['3d', '4d', '6d', '8d'], \
       'dtlz8-nbi': ['3d', '4d', '6d', '8d'], \
       'c2dtlz2': ['3d', '4d', '5d', '8d'], \
       'c2dtlz2-nbi': ['3d', '4d', '5d', '8d'], \
       'cdebmdk': ['3d', '4d', '8d'], \
       'cdebmdk-nbi': ['3d', '4d', '8d'], \
       'c0dtlz2': ['3d', '4d', '8d'], \
       'c0dtlz2-nbi': ['3d', '4d', '8d'], \
      'crash-nbi': ['3d'], 'crash-c1-nbi': ['3d'], 'crash-c2-nbi': ['3d'], \
       'carside-nbi': ['3d'], \
       'gaa-old': ['10d'], 'gaa-old-nbi': ['10d'], \
       'gaa-fmincon': ['10d'], 'gaa-fmincon-nbi': ['10d'], \
       'gaa-nsga3': ['10d'], 'gaa-nsga3-nbi': ['10d']}

constrained_pfs = ['dtlz8', 'dtlz8-nbi', 'c2dtlz2', 'c2dtlz2-nbi', 'cdebmdk', 'cdebmdk-nbi', \
                   'c0dtlz2', 'c0dtlz2-nbi', 'carside-nbi', 'gaa-old', 'gaa-old-nbi', \
                   'gaa-fmincon', 'gaa-fmincon-nbi', 'gaa-nsga3', 'gaa-nsga3-nbi']

for pf in list(pfs.keys()):
    for dim in pfs[pf]:
        fullpathf = "../data/{0:s}/{1:s}/f.csv".format(pf, dim)
        if os.path.exists(fullpathf):
            path, filenamef = os.path.split(fullpathf)
            dirs = path.split('/')
            frontname = dirs[-2]

            # load the front
            F = np.loadtxt(fullpathf, delimiter=',')
            # we need to normalize in the case of pcp, otherwise the data will not be
            # visible in some cases.
            # F = tr.normalize(F, lb = np.zeros(F.shape[1]), ub = np.ones(F.shape[1]))
            print(fullpathf, F.shape, dirs, frontname)
            
            # decide on colors and sizes
            CV, Mu, Ik = load_props(path)
            S,C,D = decide_size_color(pf, dim, CV, Mu, Ik)
            
            # all indices
            I = np.zeros(F.shape[0]).astype(bool)
            I[Ik] = True
            # Ik = knee indices, Ip = non-knee indices
            Ik,Ip = I, ~I
            
            # xtick_labels
            Lt = np.array(["$f_{{{:d}}}$".format(v) for v in range(F.shape[1])])
            # line labels
            Lp = np.array(["All other points".format(v) for v in range(Ip.shape[0])])
            Lk = np.array(["Knee points".format(v) for v in range(Ik.shape[0])])
                
            if pf in constrained_pfs:
                cbl = "Cumulative normalized CV"
                cbg = CV[Ip]
            else:
                cbl = "Centroid distance"
                cbg = D[Ip]

            figpath = os.path.join(path, "pcp.pdf")
            with plt.rc_context({"text.usetex": True, "font.size": 12}):
                ax = plt.figure().gca()

                # plot non-knee points
                pcp.plot(F[Ip], ax=ax, c=C[Ip], lw=1.00, labels=Lp, xtick_labels=Lt, colorbar=(C[Ip],cbg,cbl))
                # plot knee points
                pcp.plot(F[Ik], ax=ax, c=C[Ik], lw=1.50, labels=Lk)

                # save the fig
                plt.savefig(figpath, bbox_inches='tight', dpi=150, pad_inches=0)
                # show
                plt.show()

../data/dtlz2/3d/f.csv (1000, 3) ['..', 'data', 'dtlz2', '3d'] dtlz2

../data/dtlz2/4d/f.csv (2000, 4) ['..', 'data', 'dtlz2', '4d'] dtlz2

../data/dtlz2/8d/f.csv (4000, 8) ['..', 'data', 'dtlz2', '8d'] dtlz2

../data/dtlz2-nbi/3d/f.csv (990, 3) ['..', 'data', 'dtlz2-nbi', '3d'] dtlz2-nbi

../data/dtlz2-nbi/4d/f.csv (1771, 4) ['..', 'data', 'dtlz2-nbi', '4d'] dtlz2-nbi

../data/dtlz2-nbi/8d/f.csv (3432, 8) ['..', 'data', 'dtlz2-nbi', '8d'] dtlz2-nbi

../data/debmdk/3d/f.csv (1047, 3) ['..', 'data', 'debmdk', '3d'] debmdk

../data/debmdk/4d/f.csv (1967, 4) ['..', 'data', 'debmdk', '4d'] debmdk

../data/debmdk/8d/f.csv (4077, 8) ['..', 'data', 'debmdk', '8d'] debmdk

../data/debmdk-nbi/3d/f.csv (1143, 3) ['..', 'data', 'debmdk-nbi', '3d'] debmdk-nbi

Generators (Parallel-Coordinate Plots (PCP))¶

Notebook for generating basic PCPs of the Pareto-optimal fronts with appropriate colorings and point-sizes.¶

Generate and save plots.¶

Do PCPs¶